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Fast multi-objective Aerodynamic Optimization Using Sequential Domain Patching and multifidelity Models

JOURNAL OF AIRCRAFT 2020年第3期57卷 388-398页

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn 537 Bissel Rd Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-102 Reykjavik Iceland

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

关键词： Aerodynamic Shape Optimization Morphing Airfoil CFD Simulation Surrogate Model Reynolds Averaged Navier Stokes multi objective evolutionary algorithms High Performance Computing multi objective Genetic Algorithm Sequential Quadratic Programming Lift Coefficient

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Development of multi-objective Six-Sigma Approach for Robust Design Optimization

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JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2008年第8期5卷 215-233页

作者： Shimoyama, Koji Oyama, Akira Fujii, Kozo Univ Tokyo Bunkyo Ku Tokyo 1138654 Japan Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa 2298510 Japan

In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds on the ideas of design for six sigma, coupled with multiobjective evolutionary algorithm, for an enhanced capability to reveal tradeoff information considering both optimality and robustness of design. While design for six sigma requires careful input parameter setting, design for multi-objective six sigma needs no such prior tuning, plus it can reveal the tradeoff information in a single optimization run. Three robust optimization problems were taken as to demonstrate the capabilities of design for multiobjective six sigma. Results indicate that design for multi-objective six sigma has a more practical and more efficient capability than the design for six sigma to reveal tradeoff design information considering both optimality and robustness of design.

关键词： Robust Design multi objective Six Sigma multi objective evolutionary algorithms Aeroplane Freestream Mach Number Aerodynamic Performance CFD Simulation Lift to Drag Ratio Beam (Structures) Airfoil Chord

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Parameter identification in synthetic biological circuits using multi-objective optimization

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IFAC-PapersOnLine 2016年第26期49卷 77-82页

作者： Boada, Y. Vignoni, A. Reynoso-Meza, G. Picó, J. Universitat Politècnica de Valencia Camino de Vera S/N Valencia46022 Spain Center for Systems Biology Dresden and Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer str. 108 Dresden01307 Germany Pontifícia Universidade Católica do Paraná Imaculada Conceição 1155 CuritibaPR80215-901 Brazil

Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological components. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. © 2016

关键词： Parameter estimation Decision making evolutionary algorithms Identification (control systems) Kinetic parameters multiobjective optimization Pareto principle Timing circuits Biological components multi criteria decision making multi objective evolutionary algorithms Pareto optimal sets Prediction errors Single objective Suitable solutions Synthetic biology

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multi-objective Optimization of Electric Vehicle Fast Charging Stations with SPEA-II

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IFAC-PapersOnLine 2015年第30期48卷 535-540页

作者： Shi, Ruifeng Lee, Kwang Y. School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Computer Engineering Baylor University TX76798 United States

During the past two decades, electrification of the transportation sector is gradually becoming a global trend due to the environmental benefits from electric vehicle (EV). Although EV charging post plays as a primary energy supply, public EV Fast Charging Station (FCS) is definitely an important backup energy supply system to the EV customers in case of urgent need. Based on current existing FCS system and the prediction of the EV charging need in the future, this paper proposes a multi-objective EV FCS layout re-planning optimization problem, which simultaneously takes the economic issue and the customers' satisfaction into consideration. To solve the problem a popular multi-objective evolutionary algorithm, Strengthen Pareto evolutionary Algorithm-II (SPEA-II), is employed in this study. Numerical case studies shows that the SPEA-II can successfully address the balance between different objectives during the re-planning FCS procedure by contrast to that of a single-objective optimization method (Improved Genetic Algorithm). © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： multiobjective optimization Customer satisfaction Electric vehicles Energy resources Fast charging (Batteries) Genetic algorithms Numerical methods Charging station planning Customers' satisfaction Environmental benefits Fast charging stations multi objective evolutionary algorithms Optimization problems Single objective optimization SPEA II

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A Novel Framework for multi-objective Optimization of Airfoils Using Invasive Weed Optimization

A Novel Framework for Multi-Objective Optimization of Airfoi...

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AIAA Aviation Forum

作者： Pant, Pranshu Joseph, Joe Omkar, S. N. Indian Inst Sci Dept Aerosp Engn Bengaluru 560012 India

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Traditionally, airfoil design has been broadly limited to experimental and empirical methods. Over the years, computing power has grown exponentially and computational methods are becoming increasingly relevant. Still, generic airfoils continue to be used in most applications. Such airfoils yield sub-optimal performance and result in compromises in the overall design of the aircraft. With the advent of modern high-performance computing systems and metaheuristic optimization algorithms, optimizing airfoils for their specific use cases has become highly feasible. This paper presents a novel optimization framework for airfoils using the Invasive Weed Optimisation algorithm. The presented framework can be implemented using single or multiple objectives with the multi-objective functionality being realized through integration with NSGA-II. Additionally, this framework has the unique ability to operate in two fidelity modes in order to cater to a range of computational capabilities. The low fidelity mode is coupled with XFOIL while the high fidelity mode utilizes a RANS CFD solver on OpenFOAM. To depict the prowess of this framework, two test cases have been shown. The resulting optimized airfoils perform exceedingly well in their use case as compared to conventional airfoils, thereby validating the efficacy of this framework.

关键词： Airfoil Databases Computational Fluid Dynamics XFoil Reynolds Averaged Navier Stokes High Performance Computing Systems Optimization Algorithm Computational Methods Airfoil Geometry multi objective evolutionary algorithms Genetic Algorithm

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Preliminary Study of multi-objective Air Traffic Optimization by Using Step Back Cellular Automaton

Preliminary Study of Multi-Objective Air Traffic Optimizatio...

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AIAA Aviation Forum and Exposition

作者： Sekine, Katsuhiro Tatsukawa, Tomoaki Nagaoka, Shinsuke Fuji, Kozo Tokyo Univ Sci Dept Management Sci Tokyo 1258585 Japan Tokyo Univ Sci Dept Informat & Comp Technol Tokyo 1258585 Japan

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

On the background of growing demand in air traffic, over-capacity of airports occur. When the number of aircraft exceeds the capacity of an airport, aircraft have to take a detour or keep making circles on the particular points to adjust the arrival time. However, these time adjustments trigger not only the arrival delay but also increase of fuel consumption. The objective of this study is to make efficient flight schedules with less congestion and enough resilience against traffic problems. In this study, a multi-objective air traffic optimization is conducted. The objective functions are minimization of averaged arrival-delay, the ratio of the number of delayed aircraft, and the mean fuel consumption. The design variables are the departure-time offsets of each domestic aircraft toward Tokyo International Airport. As a simulator, cellular automaton is adopted. As the result of optimization, a lot of optimal solutions are obtained. These solutions can decrease not only the arrival delay but also fuel consumption. This indicates that the optimization of ground movements in airports contribute to more efficient air-traffic operation.

关键词： Fuel Consumption Base of Aircraft Data multi objective evolutionary algorithms Air Traffic Flow Management Federal Aviation Administration Airspace Aircraft Performance Aircraft Data Flight Number Scatter Plot

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Thermal Performance Analysis and Optimization of Microjet Cooling of High-Power Light-Emitting Diodes

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2013年第2期27卷 235-245页

作者： Husain, Afzal Kim, Sun-Min Kim, Jun-Hee Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

The present study investigates the thermal and hydraulic performance of the microjet array cooling system for thermal management of a high-power light-emitting diode array. Three-dimensional numerical analyses were performed for steady incompressible turbulent flow and conjugate heat transfer through a finite volume solver. The performances of several microjet array configurations, viz., 1-jet, 4-jet, 9-jet, 13-jet, and 16-jet arrays, were analyzed at two flow rates and top-cavity heights. The design optimization of a 4-jet array cooling system was performed using a multi-objective evolutionary algorithm. For the optimization, two design variables, viz., ratio of the nozzle diameter and height of the top cavity and ratio of the height and length of the top cavity, were selected. The thermal resistance and pressure drop were selected as the objective functions of the design. The Pareto-optimal solutions were obtained and discussed in view of the thermal resistance and pressure drop and sensitivity of design variables to objective functions.

关键词： Thermal Performance Diodes Heat Transfer Coefficients multi objective evolutionary algorithms Thermal Management Turbulent Flow Numerical Modeling Thermoelectric Cooling Total Mass Flow Rate Heat Transfer Fluid

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Comparison of Optimization Strategies for High-Lift Design

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JOURNAL OF AIRCRAFT 2017年第2期54卷 642-658页

作者： Iannelli, Pierluigi Moens, Frederic Minervino, Mauro Ponza, Rita Benini, Ernesto Ctr Italiano Ric Aerosp Fluid Mech Dept Aerodynam Technol & Icing Lab I-81043 Capua Italy Off Natl Etud & Rech Aerosp Appl Aerodynam Dept F-92190 Meudon France Piaggio Aero Ind Res & Dev Dept I-17038 Villanova Dalbenga Italy Ctr Italiano Ric Aerosp Fluid Mech Dept Multidisciplinary Anal & Design Lab I-81043 Capua Italy Univ Padua Dept Ind Engn I-35131 Padua Italy

The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, funded by the European Commission under the Seventh Framework Programme, aimed at improving the aerodynamics of high-lift systems by developing, in a coordinated approach, both efficient numerical design strategies and measurement techniques for cryogenic conditions. Within the Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, different partners used several numerical automatic optimization strategies for high-lift system design purposes. A realistic multi-objective and multipoint optimization problem was defined and solved by adopting different flow model dimensionality, meshing techniques, geometry parameterization, and optimization strategies. Special attention was devoted to perform a fair comparison of the results, and useful information was obtained about trends, pros, and cons of the approaches used. The outcome of these activities is that an efficient design process can be set up through decoupling of the original multi-objective problem into several, sequential suboptimization processes. Nevertheless, several decoupling possibilities exist, and the most efficient one can be identified only on the bases of preanalysis or preknowledge of the specific problem. Second, the exercise carried out demonstrated the maturity and feasibility of a full three-dimensional automatic high-lift design.

关键词： Aerodynamic Force Coefficients CFD Simulation Aerodynamic Efficiency Flap Deflection Reynolds Averaged Navier Stokes multi objective evolutionary algorithms multi objective Genetic Algorithm Two Dimensional Flow Optimization Algorithm Navier Stokes Solver

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Optimization of Rotating Cooling Channel with Pin Fins Downstream of Turning Region

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2012年第1期26卷 85-97页

作者： Moon, Mi-Ae Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

The heat transfer performance of a rotating two-pass rectangular channel with staggered pin-fin arrays is optimized through Reynolds-averaged Navier-Stokes analysis, a surrogate method, and a multi-objective evolutionary algorithm. As the Pareto-optimal front produces a set of optimal solutions, the trends in the objective functions in relation to the design variables are predicted by a hybrid multi-objective evolutionary algorithm. Two objective functions related to heat transfer and friction loss, respectively, have been considered to estimate the performance of the cooling channel in turbine blades. Two design variables are selected, viz., the ratio of the diameter to height of the pin fins and the ratio of the streamwise spacing between the pin fins to height of the pin fins, and 20 designs are generated by Latin hypercube sampling. A response surface approximation model as a surrogate model is constructed for each objective function, and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto-optimal front. The Reynolds number that is based on the hydraulic diameter of the channel is fixed at 10,000, while the rotation number is held constant at 0.15. The turning region upstream of the pin fins enhances the heat transfer through the channel, since a strong vortical flow structure is created in this region. At some optimal points on the Pareto-optimal front, it is found that the present multi-objective optimization enhances both the heat transfer performance and the pressure-loss characteristics. The effects of the rotation number on the heat transfer and pressure loss are also discussed.

关键词： Enhancing Heat Transfer Rate Reynolds Averaged Navier Stokes multi objective evolutionary algorithms multigrid Methods Reynolds Number Turbine Inlet Temperature Friction Factor Aspect Ratio Horseshoe Vortex Coolant

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Surrogate-Assisted Differential Evolution Using Knowledge-Transfer-Based Sampling for Expensive Optimization Problems

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AIAA JOURNAL 2022年第5期60卷 3251-3266页

作者： Long, Teng Ye, Nianhui Shi, Renhe Wu, Yufei Tang, Yifan Beijing Inst Technol Sch Aerosp Engn Beijing Peoples R China Beijing Inst Technol Key Lab Dynam & Control Flight Vehicle Minist Educ Beijing 100081 Peoples R China Simon Fraser Univ Sch Mech Syst Engn Surrey BC V3T 0A3 Canada

To sufficiently reuse the knowledge from previous optimization efforts, a surrogate-assisted differential evolution using knowledge-transfer-based sampling (denoted as SADE-KTS) method is proposed for solving expensive black-box optimization problems. In SADE-KTS, a novel knowledge-transfer-based sampling method is integrated with the differential evolution framework to generate promising initial sample points. In this way, a least-squares support vector machine classifier is constructed based on the prior optimization knowledge database to calibrate the initial sample points adaptively, which improves the exploration performance via transferring the existed optimization efforts to the current optimization task. Moreover, the radial basis function and kriging surrogates are employed to replace the expensive simulation models for evolutionary operations, where the tailored differential evolution operators are cooperated with the sequential quadratic programming optimizer to lead the search to the global optimum efficiently. A number of numerical benchmarks are tested to illustrate the optimization capacity of SADE-KTS compared with several competitive optimization algorithms. Finally, SADE-KTS is applied to an airfoil aerodynamic knowledge-based optimization problem considering the existed optimization knowledge, which demonstrates the practicality and effectiveness of the proposed SADE-KTS in engineering practices.

关键词： Airfoil Sequential Quadratic Programming Support Vector Machine Black Box Numerical Simulation multi objective evolutionary algorithms Lagrange multipliers Lift Coefficient Finite Element Analysis multidisciplinary Design Optimization

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